# W\&L Law Journal Rankings Scraper (`jungle_synthesizer/wlu-law-journal-rankings-scraper`) Actor Scrapes the authoritative Washington and Lee University law-journal ranking: rank, combined score, impact factor, citations, currency factor, ISSN, and journal URL for ~1,800 ranked law journals. - **URL**: https://apify.com/jungle\_synthesizer/wlu-law-journal-rankings-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Education, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## W&L Law Journal Rankings Scraper Scrapes the Washington and Lee University law-journal ranking — the most widely cited citation-based ranking of legal publications — and returns structured data for ~1,800 ranked journals. W&L publishes annually at `managementtools4.wlu.edu`. The ranking covers combined score, impact factor, journal citations, and currency factor across the 5-year ranking window. Each record also includes ISSN, journal URL, country, and a subject-area classification. Getting this data out of the site involves an ASP.NET form that doesn't respond to raw HTTP requests. This actor handles that, which is why you're here. ### Features - Extracts all ~1,800 ranked law journals from the W&L ranking grid - Returns combined score, impact factor, citation count, and currency factor per journal - Fetches supplemental detail — ISSN, journal URL, country, publisher school — from the site's undocumented JSON endpoint - Handles dynamic column header detection (column order varies by criteria selection) - `maxItems` cap to limit output during development or testing - No proxies required — W&L has no anti-bot measures ### How It Works 1. Loads the W&L ranking default page in a headless Chromium browser 2. Selects "All" in the ShowNum dropdown to expand from the default Top-10 view 3. Enables all four ranking criteria checkboxes (Combined Score, Impact Factor, Journal Cites, Currency Factor) 4. Submits the form and waits for the full grid to render 5. Extracts rank, journal name, score columns, and internal journal IDs from the results grid 6. Fetches supplemental JSON for each journal in parallel batches from the site's detail endpoint 7. Returns one structured record per journal ### Input Configuration | Field | Type | Default | Description | |-------|------|---------|-------------| | `maxItems` | integer | `10` | Maximum number of records to return | **Example input:** ```json { "maxItems": 100 } ```` To get all ~1,800 ranked journals, set `maxItems` to `1800` or higher. ### Output Each record contains the journal's ranking data and supplemental detail fields. **Example record:** ```json { "rank": 1, "journal_name": "Harvard Law Review", "publisher_school": "Harvard Law School", "issn": "0017-811X", "subject_area": "General/Flagship", "country": "United States", "combined_score": 9.842, "impact_factor": 9.901, "cites": 22104, "currency_factor": 9.724, "journal_url": "https://harvardlawreview.org", "ranking_year": "20-24" } ``` **Output field reference:** | Field | Type | Description | |-------|------|-------------| | `rank` | integer | Overall rank in the W\&L combined score ranking | | `journal_name` | string | Full journal title | | `publisher_school` | string | Sponsoring institution, parsed from the journal's address block | | `issn` | string | Print or electronic ISSN | | `subject_area` | string | `General/Flagship` (G) or `Specialized` (S) | | `country` | string | Country of publication | | `combined_score` | number | W\&L combined citation score for the ranking period | | `impact_factor` | number | Impact factor component of the combined score | | `cites` | integer | Total journal citations during the ranking window | | `currency_factor` | number | Currency factor component of the combined score | | `journal_url` | string | Publisher URL for the journal | | `ranking_year` | string | Ranking period, e.g. `20-24` for the 2020–2024 window | ### 🔍 FAQ **How do I scrape the W\&L law journal rankings?** W\&L Law Journal Rankings Scraper connects to `managementtools4.wlu.edu`, submits the ranking form with all criteria enabled, and returns all ranked journals. Set `maxItems` to the number of journals you want, or leave it high to capture the full list. **What data can I get from the W\&L law journal ranking?** W\&L Law Journal Rankings Scraper returns rank, combined score, impact factor, citation count, currency factor, ISSN, journal URL, country, publisher school, and ranking year — one record per journal. The fine-grained subject classification (Tax, IP, Administrative, etc.) is a search filter only and is not exposed per-journal in the site's data. **How much does W\&L Law Journal Rankings Scraper cost to run?** W\&L Law Journal Rankings Scraper runs on a flat PPE profile. A full run of ~1,800 journals typically completes in 3–5 minutes and costs well under $0.05. The site needs no proxy, which keeps costs down. **Does W\&L Law Journal Rankings Scraper need proxies?** No. Washington and Lee's ranking site has no anti-bot measures. The scraper uses a headless browser only because the ASP.NET form requires JavaScript execution to load results — not because of any blocking. **Can I get data for a specific subject area or practice area?** W\&L Law Journal Rankings Scraper returns all ranked journals. The W\&L site's subject-area filter is not exposed as individual journal metadata, so you can't pre-filter by subject. Filter by `subject_area` (General/Flagship vs. Specialized) or by keyword in `journal_name` after extracting. ### Need More Features? Need custom fields, filters, or a different target site? [File an issue](https://console.apify.com/actors/issues) or get in touch. ### Why Use W\&L Law Journal Rankings Scraper? - **Authoritative source** — W\&L is the standard citation-based ranking used by law schools, faculty, and researchers for journal prestige evaluation. - **Complete coverage** — Returns all ~1,800 ranked journals with four scoring dimensions, not just the default Top-10 view the site shows without help. - **No proxy overhead** — Runs on basic compute with no residential proxy cost, because the site doesn't require it. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of records to scrape ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10 } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/wlu-law-journal-rankings-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/wlu-law-journal-rankings-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 10 }' | apify call jungle_synthesizer/wlu-law-journal-rankings-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/wlu-law-journal-rankings-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "W&L Law Journal Rankings Scraper", "description": "Scrapes the authoritative Washington and Lee University law-journal ranking: rank, combined score, impact factor, citations, currency factor, ISSN, and journal URL for ~1,800 ranked law journals.", "version": "0.1", "x-build-id": "eF81eVjZP558Bggag" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~wlu-law-journal-rankings-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-wlu-law-journal-rankings-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~wlu-law-journal-rankings-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-wlu-law-journal-rankings-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~wlu-law-journal-rankings-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-wlu-law-journal-rankings-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of records to scrape", "default": 10 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```